Urban building information is vital to urban planning and urban climate studies. However, for some cities, especially for those at developing countries, the lack of effective urban building information remains an issue. Remote sensing technology makes it possible to provide urban building information in a large area. In particular, with the recent launch of new high-resolution satellites (e.g., high spatial resolution world-view satellites and meters' resolution Terra-SAR satellites), more high quality satellite data are now available to support studies in which urban building information is retrieved. However, due to a complexity of building types (e.g., shapes, sizes, colors and textures) and their high similarity and proximity to other on-ground targets, the automatic and accurate extraction of building information from satellite images for large high-density urban area is still a challenge task. Therefore, it is necessary to develop fast and efficient 3D building extraction technology for large and complex urban area.
Earlier studies mainly focused on 2D building footprint extraction using optical satellite images in which various advanced image classification/feature extraction approaches have been developed. In these approaches, the spectral and shape information on buildings are the most widely used features for the building footprint extraction. To improve footprint extraction accuracy, other important features of buildings, like corners, edges, shadows and the heights of building, have been exploited.
With the development of advanced satellite technology (e.g., stereo photogrammetry technology using pairs of optical images (stereo images), synthetic aperture radar (SAR-) technology, and light detection and ranging data (LiDAR) technology), automatic building extraction technology, and especially building height extraction techniques, have risen to new levels. Stereo technology uses a pair of satellite images (stereo images) from different observation angles to retrieve the heights of buildings over the same area using photogrammetry technology. The main difficulty with stereo technology, however, is automatically retrieving the corresponding points of the same objects from a pair of satellite images (denoted stereoscopic matching technology). Thus, other advanced stereoscopic matching methods have been developed to improve height retrieval accuracy and automatic mapping capabilities. Further, some ancillary data have been exploited to improve height retrieval accuracy, e.g., by using multiple stereo images or other ancillary data, such as the digital elevation model.
SAR data can record distance information from a satellite to the Earth's surface, from which the height information of on-ground targets can be retrieved using inference SAR images or monoscopic SAR images. Similar to stereo images, stereo SAR images can also be applied to retrieve the heights of buildings by using radargrammetry technology. However, due to the different imaging mechanisms distinguishing SAR images from optical images, the conventional building footprint extraction methods used for optical images are not suitable for SAR images. Studies on building footprint extraction using SAR images have primarily focused on the exploitation of other important building features, such as statistical texture information, bright linear lines, and building shadows, based on which evidence-based approaches have been used to combine the retrieved features for building footprint extraction.
In recent years, 3D building extraction involving building footprint extraction and building height retrieval is proposed to improve the building extraction performance. Stereo and SAR images have relatively lower accuracy in retrieving building data but their costs are lower, making them quite suitable and practical for large area applications. In addition, the combination of optical satellite images and SAR data present a much better recognition performance for all kind of buildings.